Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/datalab-atom/evoany/write-methodnpx skills add DataLab-atom/EvoAny --skill write-methodgit clone --depth 1 https://github.com/DataLab-atom/EvoAnyWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00029 | $0.00802 |
| Opus 5 | $0.00015 | $0.00401 |
| Sonnet 5 | $0.00006 | $0.00160 |
| Haiku 4.5 | $0.00003 | $0.00080 |
Grade A, and why
write-method scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/write-method — Method Chapter Writing
D1: Method chapter generation — transforms the derivation forest's deep motivation and contribution structure into a rigorous methodology LaTeX section.
Purpose
Read the completed derivation forest (from C-layer), extract the deep motivation Q and primary contribution branches, and generate a well-structured Method section in LaTeX format suitable for academic submission.
Usage
/write-method <forest_id> [--venue <venue_name>]
Examples:
/write-method exp-2024-run-01 --venue NeurIPS/write-method my-forest --venue ICML
Prerequisites
Before running this skill, ensure:
- The derivation forest has converged (status = "converging" or "done")
- At least one convergence point has been verified
- All contributing branches have been recorded via
research_record_contribution
Behavior
Step 1: Read the Derivation Forest
- Call
research_get_forest(forest_id)to retrieve the full forest state - Extract:
- All convergence_points with
verification_status === "verified" - For each point: the deep question Q, contributing nodes, literature references
- All contributions with
level === "primary"
- All convergence_points with
Step 2: Synthesize the Method Chapter
Based on the forest data, generate a LaTeX method chapter covering:
- Problem Formalization — What is the deep problem Q being solved?
- Technical Approach — How does the evolved code address Q?
- Key Mechanisms — What are the primary contributions? (from converged branches)
- Relationship to Existing Methods — How does this differ from related work?
Step 3: Write to File
- Determine output path:
<repo>/research/paper/sections/method.tex - Call
research_get_forestwith the forest ID to get the repo path - Write the LaTeX content to the file
- Call
bib_appendto add any new citations from the forest's literature references
Output Format
\section{Method}
\label{sec:method}
\subsection{Problem Definition}
% Content addressing the deep motivation Q
\subsection{Technical Approach}
% How the evolved approach works
\subsection{Key Mechanisms}
% Primary contributions from converged branches
\subsection{Theoretical Analysis (if applicable)}
% Any formal guarantees or analysis
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 105 lines · 29 tokens per session scan A eb0941fe7c03
write-method is a skill published in the GitHub repository DataLab-atom/EvoAny (37 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 29 tokens to every session and 802 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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